Casal2 Test Case MCMC comparisons for BCO
1 BCO comparison of CASAL and Casal2 model configurations
This document compares the results of at least 2 CASAL model configurations (base and at least one sensitivity) and up to 8 Casal2 model configurations (3 BetaDiff, 2 CppAD, and 3 ADOL-C).
The CASAL model sensitivity 1 has a smaller minimisation tolerance value than the CASAL base model (1e-7 vs. 2e-5).
The Casal2 ADOL-C and BetaDiff low tolerance models have a smaller tolerance value than the CASAL base model (1e-7 vs. 2e-5). The Casal2 CppAD models have a tolerance value of 1e-9.
1.1 BCO model characteristics
The main characteristics of the Test Case BCO (ling) CASAL model are:
- one stock, ages 3 - 20
- one area
- years 1900 - 2019, projection year 2020
- one time step
- two stock categories: male and female
- natural mortality-at-age is 0.14 for males and 0.20 for females
- sex-specific logistic maturity
- sex-specific von Bertalanffy age-length relationship
- one length-weight relationship (\(W = aL^b\))
- Beverton-Holt stock-recruitment relationship, with steepness (h) 0.75 and \(\sigma_R\) 0.6
- no ageing error
- one survey with sex-specific age-based logistic selectivity
- three fisheries, all with sex-specific length-based logistic selectivity and retained/discarded catches
Observation data include:
- survey index
- fishery CPUE index
- fishery proportions-at-length
Parameters estimated include:
- B0
- male \(L_{\infty}\)
- male age-length CV2
- sex-specific survey logistic selectivity (a50 and ato95)
- YCS (recruitment deviations)
1.2 BCO model MCMC specifications
The CASAL MCMC options include
- start 0
- keep 2500
- length 4000000
- stepsize 0.02
- adaptive_stepsize True
- adapt_at 10000 50000 150000 250000 450000 750000 1400000
- df 2
- burn_in 1000
- proposal_t True
The Casal2 ADOL-C and BetaDiff MCMC options include
- start 0
- keep 2500
- length 4000000
- step_size 0.02
- adapt_stepsize_at 10000 50000 150000 250000 450000 750000 1400000
- proposal_distribution t
- adapt_stepsize_method double_half
The Casal2 CppAD MCMC runs use the BetaDiff covariance matrices since Casal2 does not output the covariance matrix from CppAD minimisation. The Casal2 CppAD MCMC options include
- start 0
- keep 2500
- length 5500000 (originally 4M)
- step_size 0.02
- adapt_stepsize_at 10000 50000 150000 250000 450000 750000 1400000
- proposal_distribution t
- adapt_stepsize_method double_half
- adapt_covariance_matrix_at 1500000
1.3 R environment
## [1] "Mon Aug 3 17:02:18 2020"
## R version 3.6.0 (2019-04-26)
## Platform: x86_64-redhat-linux-gnu (64-bit)
## Running under: CentOS Linux 7 (Core)
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## Matrix products: default
## BLAS/LAPACK: /usr/lib64/R/lib/libRblas.so
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## locale:
## [1] LC_CTYPE=en_NZ.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_NZ.UTF-8 LC_COLLATE=en_NZ.UTF-8
## [5] LC_MONETARY=en_NZ.UTF-8 LC_MESSAGES=en_NZ.UTF-8
## [7] LC_PAPER=en_GB.UTF_8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_NZ.UTF-8 LC_IDENTIFICATION=C
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] casal2_1.0 casal_2.30 devtools_2.3.1 usethis_1.6.1 rlist_0.4.6.1
## [6] ggthemes_4.2.0 gridExtra_2.3 coda_0.19-3 ggmcmc_1.4.1 ggplot2_3.3.2
## [11] tidyr_1.1.0 dplyr_1.0.1
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## loaded via a namespace (and not attached):
## [1] tidyselect_1.1.0 xfun_0.16 remotes_2.2.0 purrr_0.3.4
## [5] lattice_0.20-38 testthat_2.3.2 colorspace_1.4-1 vctrs_0.3.2
## [9] generics_0.0.2 htmltools_0.5.0 yaml_2.2.1 rlang_0.4.7
## [13] pkgbuild_1.1.0 pillar_1.4.6 glue_1.4.1 withr_2.2.0
## [17] RColorBrewer_1.1-2 sessioninfo_1.1.1 lifecycle_0.2.0 plyr_1.8.6
## [21] stringr_1.4.0 munsell_0.5.0 gtable_0.3.0 evaluate_0.14
## [25] memoise_1.1.0 knitr_1.29 GGally_1.4.0 callr_3.4.3
## [29] ps_1.3.3 fansi_0.4.1 Rcpp_1.0.5 scales_1.1.1
## [33] backports_1.1.8 desc_1.2.0 pkgload_1.1.0 fs_1.5.0
## [37] digest_0.6.25 stringi_1.4.6 processx_3.4.3 grid_3.6.0
## [41] rprojroot_1.3-2 cli_2.0.2 tools_3.6.0 magrittr_1.5
## [45] tibble_3.0.3 crayon_1.3.4 pkgconfig_2.0.3 ellipsis_0.3.1
## [49] data.table_1.13.0 prettyunits_1.1.1 assertthat_0.2.1 rmarkdown_2.3
## [53] reshape_0.8.8 R6_2.4.1 compiler_3.6.01.4 CASAL and Casal2 model output
# source('../../R-functions/report_read_in_CASAL_MPD_files.R')
source('../../R-functions/report_read_in_CASAL_MCMC_files.R')source('../../R-functions/report_read_in_Casal2_MPD_files.R')
source('../../R-functions/report_read_in_Casal2_MCMC_files.R')1.5 MCMC diagnostics
1.5.1 Diagnostics for the CASAL and Casal2 model MCMC results
For the diagnostics below, the last 1000 samples for each chain are input into the diagnostic functions.
